Meta-analysis approach identifies candidate genes and associated molecular networks for type-2 diabetes mellitus.
Meta-analysis approach identifies candidate genes and associated molecular networks for type-2 diabetes mellitus.
复制标题
DOI：
10.1186/1471-2164-9-310
复制
发表时间：
2008-06-30
期刊：
影响因子：
4.4
通讯作者：
Herwig R
中科院分区：
文献类型：
--
作者：
Rasche A;Al-Hasani H;Herwig R
复杂人类疾病的多功能基因组学数据已经发表，并由世界各地的研究人员提供。这些研究的主要目标是详细分析疾病的特定方面。互补的荟萃分析方法试图通过使用统计方法整合和组合这些单独的研究来提取疾病基因和相互作用网络的超集。在这里，我们报告的荟萃分析方法，整合数据的异质性来源领域的2型糖尿病（T2 DM）。不同的数据源，如DNA微阵列和，补充，定性数据涵盖了几个人类和小鼠组织的整合和分析与Bootstrap评分方法，以提取疾病相关的基因。荟萃分析的目的是双重的：一方面，它确定了一组基因与整体疾病的相关性，表明共同的，组织无关的过程与疾病有关;另一方面，它确定了基因显示特定的改变相对于一个单一的研究。使用随机抽样方法，我们计算了人类和小鼠多个组织中213个T2 DM基因的核心集，包括众所周知的基因，如Pdk 4，Adipoq，Scd，Pik 3r 1，Socs 2，这些基因监测T2 DM的重要标志，例如肥胖和胰岛素抵抗之间的密切关系，以及大部分（128）尚未表征的新候选基因。此外，我们探索了功能信息，并确定了与这组核心基因相关的细胞网络，如通路信息，蛋白质-蛋白质相互作用和基因调控网络。此外，我们建立了一个网络界面，以允许用户筛选T2 DM相关性的任何非相关基因。在我们的论文中，我们通过对现有数据源的荟萃分析确定了213个T2 DM候选基因的核心集。我们已经探索了这些基因与疾病相关信息的关系，并且使用富集分析，我们已经确定了不同细胞信息层上的生物网络，例如信号传导和代谢途径，基因调控网络和蛋白质-蛋白质相互作用。Web界面可通过访问。
Multiple functional genomics data for complex human diseases have been published and made available by researchers worldwide. The main goal of these studies is the detailed analysis of a particular aspect of the disease. Complementary, meta-analysis approaches try to extract supersets of disease genes and interaction networks by integrating and combining these individual studies using statistical approaches. Here we report on a meta-analysis approach that integrates data of heterogeneous origin in the domain of type-2 diabetes mellitus (T2DM). Different data sources such as DNA microarrays and, complementing, qualitative data covering several human and mouse tissues are integrated and analyzed with a Bootstrap scoring approach in order to extract disease relevance of the genes. The purpose of the meta-analysis is two-fold: on the one hand it identifies a group of genes with overall disease relevance indicating common, tissue-independent processes related to the disease; on the other hand it identifies genes showing specific alterations with respect to a single study. Using a random sampling approach we computed a core set of 213 T2DM genes across multiple tissues in human and mouse, including well-known genes such as Pdk4, Adipoq, Scd, Pik3r1, Socs2 that monitor important hallmarks of T2DM, for example the strong relationship between obesity and insulin resistance, as well as a large fraction (128) of yet barely characterized novel candidate genes. Furthermore, we explored functional information and identified cellular networks associated with this core set of genes such as pathway information, protein-protein interactions and gene regulatory networks. Additionally, we set up a web interface in order to allow users to screen T2DM relevance for any – yet non-associated – gene. In our paper we have identified a core set of 213 T2DM candidate genes by a meta-analysis of existing data sources. We have explored the relation of these genes to disease relevant information and – using enrichment analysis – we have identified biological networks on different layers of cellular information such as signaling and metabolic pathways, gene regulatory networks and protein-protein interactions. The web interface is accessible via .
登录
查看更多内容
影响因子：
7.7
作者：
Biddinger, SB;Almind, K;Kahn, CR
通讯作者：
Kahn, CR
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
7.7
作者：
Gu, HF;Abulaiti, A;Efendic, S
通讯作者：
Efendic, S
影响因子：
56.9
作者：
Frayling, Timothy M.;Timpson, Nicholas J.;McCarthy, Mark I.
通讯作者：
McCarthy, Mark I.
影响因子：
64.5
作者：
Gunton, JE;Kulkarni, RN;Kahn, CR
通讯作者：
Kahn, CR